Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction. (arXiv:2103.11414v2 [cs.LG] UPDATED)

Graph neural networks have been used for a variety of learning tasks, such as
link prediction, node classification, and node clustering. Among them, link
prediction is a relatively under-studied graph learning task, with current
state-of-the-art models based on one- or two-layer of shallow graph
auto-encoder (GAE) architectures. In this paper, we focus on addressing a
limitation of current methods for link prediction, which can only use shallow
GAEs and variational GAEs, and creating effective methods to deepen
(variational) GAE architectures …

arxiv graph lg link prediction prediction